Overview

Brought to you by YData

Dataset statistics

Number of variables52
Number of observations115787
Missing cells880842
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory293.4 MiB
Average record size in memory2.6 KiB

Variable types

Boolean3
Numeric7
Categorical33
Text7
Path1
DateTime1

Alerts

trafficSource.isTrueDirect has constant value "True" Constant
device.screenResolution has constant value "not available in demo dataset" Constant
screenSize has constant value "medium" Constant
device.mobileDeviceBranding has constant value "not available in demo dataset" Constant
device.mobileInputSelector has constant value "not available in demo dataset" Constant
device.mobileDeviceMarketingName has constant value "not available in demo dataset" Constant
device.operatingSystemVersion has constant value "not available in demo dataset" Constant
device.flashVersion has constant value "not available in demo dataset" Constant
totals.visits has constant value "1" Constant
geoNetwork.networkLocation has constant value "not available in demo dataset" Constant
trafficSource.adwordsClickInfo.isVideoAd has constant value "False" Constant
browserMajor has constant value "not available in demo dataset" Constant
device.browserSize has constant value "not available in demo dataset" Constant
socialEngagementType has constant value "Not Socially Engaged" Constant
locationZone has constant value "8" Constant
device.mobileDeviceModel has constant value "not available in demo dataset" Constant
totals.bounces has constant value "1.0" Constant
device.language has constant value "not available in demo dataset" Constant
device.browserVersion has constant value "not available in demo dataset" Constant
device.screenColors has constant value "not available in demo dataset" Constant
new_visits has constant value "1.0" Constant
browser is highly overall correlated with osHigh correlation
device.isMobile is highly overall correlated with deviceType and 1 other fieldsHigh correlation
deviceType is highly overall correlated with device.isMobile and 1 other fieldsHigh correlation
gclIdPresent is highly overall correlated with trafficSource.adwordsClickInfo.adNetworkType and 5 other fieldsHigh correlation
geoNetwork.continent is highly overall correlated with geoNetwork.subContinentHigh correlation
geoNetwork.subContinent is highly overall correlated with geoNetwork.continentHigh correlation
os is highly overall correlated with browser and 3 other fieldsHigh correlation
pageViews is highly overall correlated with purchaseValue and 1 other fieldsHigh correlation
purchaseValue is highly overall correlated with pageViews and 4 other fieldsHigh correlation
sessionId is highly overall correlated with sessionStart and 2 other fieldsHigh correlation
sessionStart is highly overall correlated with sessionId and 2 other fieldsHigh correlation
totalHits is highly overall correlated with pageViews and 1 other fieldsHigh correlation
trafficSource.adwordsClickInfo.adNetworkType is highly overall correlated with gclIdPresent and 7 other fieldsHigh correlation
trafficSource.adwordsClickInfo.page is highly overall correlated with gclIdPresent and 1 other fieldsHigh correlation
trafficSource.adwordsClickInfo.slot is highly overall correlated with gclIdPresent and 6 other fieldsHigh correlation
trafficSource.campaign is highly overall correlated with gclIdPresent and 4 other fieldsHigh correlation
trafficSource.medium is highly overall correlated with gclIdPresent and 2 other fieldsHigh correlation
userChannel is highly overall correlated with gclIdPresent and 4 other fieldsHigh correlation
browser is highly imbalanced (71.9%) Imbalance
trafficSource.campaign is highly imbalanced (91.5%) Imbalance
gclIdPresent is highly imbalanced (77.2%) Imbalance
trafficSource.adwordsClickInfo.page is highly imbalanced (94.5%) Imbalance
trafficSource.isTrueDirect has 73050 (63.1%) missing values Missing
trafficSource.adContent has 112826 (97.4%) missing values Missing
trafficSource.keyword has 71729 (61.9%) missing values Missing
trafficSource.adwordsClickInfo.slot has 111518 (96.3%) missing values Missing
trafficSource.adwordsClickInfo.isVideoAd has 111518 (96.3%) missing values Missing
trafficSource.adwordsClickInfo.adNetworkType has 111518 (96.3%) missing values Missing
trafficSource.adwordsClickInfo.page has 111518 (96.3%) missing values Missing
trafficSource.referralPath has 73099 (63.1%) missing values Missing
totals.bounces has 68666 (59.3%) missing values Missing
new_visits has 35392 (30.6%) missing values Missing
purchaseValue is highly skewed (γ1 = 54.01703551) Skewed
sessionNumber is highly skewed (γ1 = 20.42031906) Skewed
purchaseValue has 91990 (79.4%) zeros Zeros

Reproduction

Analysis started2025-07-17 09:59:56.234385
Analysis finished2025-07-17 10:00:34.722992
Duration38.49 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

trafficSource.isTrueDirect
Boolean

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing73050
Missing (%)63.1%
Memory size4.6 MiB
True
42737 
(Missing)
73050 
ValueCountFrequency (%)
True 42737
36.9%
(Missing) 73050
63.1%
2025-07-17T15:30:34.841214image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

purchaseValue
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6766
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26422442
Minimum0
Maximum2.31295 × 1010
Zeros91990
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-07-17T15:30:35.044919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.05672 × 108
Maximum2.31295 × 1010
Range2.31295 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0517255 × 108
Coefficient of variation (CV)7.7650867
Kurtosis4646.0712
Mean26422442
Median Absolute Deviation (MAD)0
Skewness54.017036
Sum3.0593753 × 1012
Variance4.2095777 × 1016
MonotonicityNot monotonic
2025-07-17T15:30:35.338566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91990
79.4%
16990000 440
 
0.4%
18990000 325
 
0.3%
33590000 301
 
0.3%
19990000 288
 
0.2%
44790000 281
 
0.2%
13590000 238
 
0.2%
55990000 209
 
0.2%
39980000 201
 
0.2%
21990000 198
 
0.2%
Other values (6756) 21316
 
18.4%
ValueCountFrequency (%)
0 91990
79.4%
10000 1
 
< 0.1%
40000 2
 
< 0.1%
90000 2
 
< 0.1%
160000 2
 
< 0.1%
200000 2
 
< 0.1%
490000 1
 
< 0.1%
500000 5
 
< 0.1%
770000 2
 
< 0.1%
790000 1
 
< 0.1%
ValueCountFrequency (%)
2.31295 × 10102
< 0.1%
1.78555 × 10102
< 0.1%
1.602375 × 10101
< 0.1%
1.32294 × 10101
< 0.1%
1.2293 × 10101
< 0.1%
1.058914 × 10101
< 0.1%
9925110000 1
< 0.1%
8677830000 2
< 0.1%
7427430000 1
< 0.1%
6996500000 2
< 0.1%

browser
Categorical

High correlation  Imbalance 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
Chrome
84312 
Safari
19160 
Firefox
 
4026
Internet Explorer
 
2152
Android Webview
 
1471
Other values (29)
 
4666

Length

Max length52
Median length6
Mean length6.4965411
Min length2

Characters and Unicode

Total characters752215
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowEdge
2nd rowChrome
3rd rowChrome
4th rowInternet Explorer
5th rowChrome

Common Values

ValueCountFrequency (%)
Chrome 84312
72.8%
Safari 19160
 
16.5%
Firefox 4026
 
3.5%
Internet Explorer 2152
 
1.9%
Android Webview 1471
 
1.3%
Edge 1214
 
1.0%
Safari (in-app) 791
 
0.7%
Opera Mini 732
 
0.6%
Samsung Internet 563
 
0.5%
Opera 546
 
0.5%
Other values (24) 820
 
0.7%

Length

2025-07-17T15:30:35.568709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome 84312
69.1%
safari 19951
 
16.3%
firefox 4026
 
3.3%
internet 2715
 
2.2%
explorer 2152
 
1.8%
android 1530
 
1.3%
webview 1471
 
1.2%
opera 1278
 
1.0%
edge 1214
 
1.0%
in-app 791
 
0.6%
Other values (29) 2650
 
2.2%

Most occurring characters

ValueCountFrequency (%)
r 119299
15.9%
e 102054
13.6%
o 92916
12.4%
m 84986
11.3%
C 84789
11.3%
h 84329
11.2%
a 42923
 
5.7%
i 29422
 
3.9%
f 23994
 
3.2%
S 20591
 
2.7%
Other values (53) 66912
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 752215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 119299
15.9%
e 102054
13.6%
o 92916
12.4%
m 84986
11.3%
C 84789
11.3%
h 84329
11.2%
a 42923
 
5.7%
i 29422
 
3.9%
f 23994
 
3.2%
S 20591
 
2.7%
Other values (53) 66912
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 752215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 119299
15.9%
e 102054
13.6%
o 92916
12.4%
m 84986
11.3%
C 84789
11.3%
h 84329
11.2%
a 42923
 
5.7%
i 29422
 
3.9%
f 23994
 
3.2%
S 20591
 
2.7%
Other values (53) 66912
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 752215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 119299
15.9%
e 102054
13.6%
o 92916
12.4%
m 84986
11.3%
C 84789
11.3%
h 84329
11.2%
a 42923
 
5.7%
i 29422
 
3.9%
f 23994
 
3.2%
S 20591
 
2.7%
Other values (53) 66912
8.9%

device.screenResolution
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:35.773706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:35.925217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

trafficSource.adContent
Text

Missing 

Distinct53
Distinct (%)1.8%
Missing112826
Missing (%)97.4%
Memory size4.6 MiB
2025-07-17T15:30:36.190260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length34
Median length32
Mean length25.079703
Min length2

Characters and Unicode

Total characters74261
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.5%

Sample

1st rowGoogle Merchandise Collection
2nd rowGoogle Merchandise Store
3rd rowGoogle Merchandise Store
4th rowGoogle Merchandise Collection
5th rowGoogle Merchandise Collection
ValueCountFrequency (%)
google 2241
23.3%
merchandise 2128
22.1%
store 1515
15.8%
collection 649
 
6.7%
ad 445
 
4.6%
display 251
 
2.6%
placement 177
 
1.8%
created 163
 
1.7%
x 158
 
1.6%
300 138
 
1.4%
Other values (66) 1753
18.2%
2025-07-17T15:30:36.990097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9983
13.4%
o 7897
 
10.6%
6657
 
9.0%
r 4363
 
5.9%
l 4333
 
5.8%
c 3485
 
4.7%
i 3333
 
4.5%
n 3256
 
4.4%
a 3182
 
4.3%
d 2960
 
4.0%
Other values (52) 24812
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74261
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9983
13.4%
o 7897
 
10.6%
6657
 
9.0%
r 4363
 
5.9%
l 4333
 
5.8%
c 3485
 
4.7%
i 3333
 
4.5%
n 3256
 
4.4%
a 3182
 
4.3%
d 2960
 
4.0%
Other values (52) 24812
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74261
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9983
13.4%
o 7897
 
10.6%
6657
 
9.0%
r 4363
 
5.9%
l 4333
 
5.8%
c 3485
 
4.7%
i 3333
 
4.5%
n 3256
 
4.4%
a 3182
 
4.3%
d 2960
 
4.0%
Other values (52) 24812
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74261
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9983
13.4%
o 7897
 
10.6%
6657
 
9.0%
r 4363
 
5.9%
l 4333
 
5.8%
c 3485
 
4.7%
i 3333
 
4.5%
n 3256
 
4.4%
a 3182
 
4.3%
d 2960
 
4.0%
Other values (52) 24812
33.4%

trafficSource.keyword
Text

Missing 

Distinct566
Distinct (%)1.3%
Missing71729
Missing (%)61.9%
Memory size6.1 MiB
2025-07-17T15:30:37.253410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length117
Median length14
Mean length14.690181
Min length3

Characters and Unicode

Total characters647220
Distinct characters135
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique450 ?
Unique (%)1.0%

Sample

1st row(not provided)
2nd row(not provided)
3rd row(not provided)
4th row(not provided)
5th row(not provided)
ValueCountFrequency (%)
not 38975
44.3%
provided 38975
44.3%
targeting 1252
 
1.4%
6qehscssdk0z36ri 1149
 
1.3%
google 1063
 
1.2%
user 924
 
1.0%
vertical 924
 
1.0%
merchandise 688
 
0.8%
automatic 670
 
0.8%
matching 670
 
0.8%
Other values (466) 2774
 
3.1%
2025-07-17T15:30:37.762014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 82897
12.8%
d 80023
12.4%
e 47200
 
7.3%
t 47002
 
7.3%
r 45314
 
7.0%
i 45146
 
7.0%
44006
 
6.8%
n 42945
 
6.6%
( 40897
 
6.3%
) 40897
 
6.3%
Other values (125) 130893
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 647220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 82897
12.8%
d 80023
12.4%
e 47200
 
7.3%
t 47002
 
7.3%
r 45314
 
7.0%
i 45146
 
7.0%
44006
 
6.8%
n 42945
 
6.6%
( 40897
 
6.3%
) 40897
 
6.3%
Other values (125) 130893
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 647220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 82897
12.8%
d 80023
12.4%
e 47200
 
7.3%
t 47002
 
7.3%
r 45314
 
7.0%
i 45146
 
7.0%
44006
 
6.8%
n 42945
 
6.6%
( 40897
 
6.3%
) 40897
 
6.3%
Other values (125) 130893
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 647220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 82897
12.8%
d 80023
12.4%
e 47200
 
7.3%
t 47002
 
7.3%
r 45314
 
7.0%
i 45146
 
7.0%
44006
 
6.8%
n 42945
 
6.6%
( 40897
 
6.3%
) 40897
 
6.3%
Other values (125) 130893
20.2%

screenSize
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
medium
115787 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters694722
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium
2nd rowmedium
3rd rowmedium
4th rowmedium
5th rowmedium

Common Values

ValueCountFrequency (%)
medium 115787
100.0%

Length

2025-07-17T15:30:38.029672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:38.258098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
medium 115787
100.0%

Most occurring characters

ValueCountFrequency (%)
m 231574
33.3%
e 115787
16.7%
d 115787
16.7%
i 115787
16.7%
u 115787
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 694722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 231574
33.3%
e 115787
16.7%
d 115787
16.7%
i 115787
16.7%
u 115787
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 694722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 231574
33.3%
e 115787
16.7%
d 115787
16.7%
i 115787
16.7%
u 115787
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 694722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 231574
33.3%
e 115787
16.7%
d 115787
16.7%
i 115787
16.7%
u 115787
16.7%

geoCluster
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
Region_2
23246 
Region_4
23229 
Region_5
23181 
Region_3
23167 
Region_1
22964 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters926296
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegion_2
2nd rowRegion_3
3rd rowRegion_2
4th rowRegion_4
5th rowRegion_3

Common Values

ValueCountFrequency (%)
Region_2 23246
20.1%
Region_4 23229
20.1%
Region_5 23181
20.0%
Region_3 23167
20.0%
Region_1 22964
19.8%

Length

2025-07-17T15:30:38.516392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:38.688136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
region_2 23246
20.1%
region_4 23229
20.1%
region_5 23181
20.0%
region_3 23167
20.0%
region_1 22964
19.8%

Most occurring characters

ValueCountFrequency (%)
R 115787
12.5%
e 115787
12.5%
g 115787
12.5%
i 115787
12.5%
o 115787
12.5%
n 115787
12.5%
_ 115787
12.5%
2 23246
 
2.5%
4 23229
 
2.5%
5 23181
 
2.5%
Other values (2) 46131
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 926296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 115787
12.5%
e 115787
12.5%
g 115787
12.5%
i 115787
12.5%
o 115787
12.5%
n 115787
12.5%
_ 115787
12.5%
2 23246
 
2.5%
4 23229
 
2.5%
5 23181
 
2.5%
Other values (2) 46131
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 926296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 115787
12.5%
e 115787
12.5%
g 115787
12.5%
i 115787
12.5%
o 115787
12.5%
n 115787
12.5%
_ 115787
12.5%
2 23246
 
2.5%
4 23229
 
2.5%
5 23181
 
2.5%
Other values (2) 46131
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 926296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 115787
12.5%
e 115787
12.5%
g 115787
12.5%
i 115787
12.5%
o 115787
12.5%
n 115787
12.5%
_ 115787
12.5%
2 23246
 
2.5%
4 23229
 
2.5%
5 23181
 
2.5%
Other values (2) 46131
 
5.0%

trafficSource.adwordsClickInfo.slot
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing111518
Missing (%)96.3%
Memory size5.4 MiB
Top
2709 
RHS
1557 
Google Display Network
 
3

Length

Max length22
Median length3
Mean length3.0133521
Min length3

Characters and Unicode

Total characters12864
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTop
2nd rowTop
3rd rowTop
4th rowTop
5th rowTop

Common Values

ValueCountFrequency (%)
Top 2709
 
2.3%
RHS 1557
 
1.3%
Google Display Network 3
 
< 0.1%
(Missing) 111518
96.3%

Length

2025-07-17T15:30:38.903941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:39.205150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
top 2709
63.4%
rhs 1557
36.4%
google 3
 
0.1%
display 3
 
0.1%
network 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 2718
21.1%
p 2712
21.1%
T 2709
21.1%
R 1557
12.1%
H 1557
12.1%
S 1557
12.1%
l 6
 
< 0.1%
e 6
 
< 0.1%
6
 
< 0.1%
g 3
 
< 0.1%
Other values (11) 33
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2718
21.1%
p 2712
21.1%
T 2709
21.1%
R 1557
12.1%
H 1557
12.1%
S 1557
12.1%
l 6
 
< 0.1%
e 6
 
< 0.1%
6
 
< 0.1%
g 3
 
< 0.1%
Other values (11) 33
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2718
21.1%
p 2712
21.1%
T 2709
21.1%
R 1557
12.1%
H 1557
12.1%
S 1557
12.1%
l 6
 
< 0.1%
e 6
 
< 0.1%
6
 
< 0.1%
g 3
 
< 0.1%
Other values (11) 33
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2718
21.1%
p 2712
21.1%
T 2709
21.1%
R 1557
12.1%
H 1557
12.1%
S 1557
12.1%
l 6
 
< 0.1%
e 6
 
< 0.1%
6
 
< 0.1%
g 3
 
< 0.1%
Other values (11) 33
 
0.3%

device.mobileDeviceBranding
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:39.424412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:39.607120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

device.mobileInputSelector
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:39.830464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:40.027422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

userId
Real number (ℝ)

Distinct100499
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61096.346
Minimum0
Maximum122276
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-07-17T15:30:40.231852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6166.6
Q130602.5
median61016
Q391621.5
95-th percentile116121.7
Maximum122276
Range122276
Interquartile range (IQR)61019

Descriptive statistics

Standard deviation35240.262
Coefficient of variation (CV)0.57679819
Kurtosis-1.1953925
Mean61096.346
Median Absolute Deviation (MAD)30518
Skewness0.0021325204
Sum7.0741626 × 109
Variance1.241876 × 109
MonotonicityNot monotonic
2025-07-17T15:30:40.581804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34711 63
 
0.1%
98503 59
 
0.1%
67566 27
 
< 0.1%
39565 27
 
< 0.1%
86785 27
 
< 0.1%
63098 24
 
< 0.1%
92957 22
 
< 0.1%
65910 21
 
< 0.1%
20308 21
 
< 0.1%
102706 21
 
< 0.1%
Other values (100489) 115475
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
11 2
< 0.1%
ValueCountFrequency (%)
122276 1
< 0.1%
122275 1
< 0.1%
122274 1
< 0.1%
122273 1
< 0.1%
122272 1
< 0.1%
122271 1
< 0.1%
122270 1
< 0.1%
122269 1
< 0.1%
122266 1
< 0.1%
122265 1
< 0.1%

trafficSource.campaign
Categorical

High correlation  Imbalance 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.3 MiB
(not set)
110058 
Data Share Promo
 
1775
AW - Dynamic Search Ads Whole Site
 
1487
1000557 | GA | US | en | Hybrid | GDN Text+Banner | AS
 
855
AW - Accessories
 
761
Other values (23)
 
851

Length

Max length68
Median length9
Mean length10.070336
Min length9

Characters and Unicode

Total characters1166014
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row(not set)
2nd row(not set)
3rd row(not set)
4th row(not set)
5th row(not set)

Common Values

ValueCountFrequency (%)
(not set) 110058
95.1%
Data Share Promo 1775
 
1.5%
AW - Dynamic Search Ads Whole Site 1487
 
1.3%
1000557 | GA | US | en | Hybrid | GDN Text+Banner | AS 855
 
0.7%
AW - Accessories 761
 
0.7%
1000557 | GA | US | en | Hybrid | GDN Remarketing 555
 
0.5%
Smart Display Campaign 87
 
0.1%
"google + redesign/Accessories March 17" All Users Similar Audiences 44
 
< 0.1%
"google + redesign/Accessories March 17" All Users 27
 
< 0.1%
Sports & Fitness/Health & Fitness Buffs 18
 
< 0.1%
Other values (18) 120
 
0.1%

Length

2025-07-17T15:30:40.985011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
not 110059
42.6%
set 110058
42.6%
10302
 
4.0%
aw 2258
 
0.9%
data 1775
 
0.7%
share 1775
 
0.7%
promo 1775
 
0.7%
site 1487
 
0.6%
whole 1487
 
0.6%
ads 1487
 
0.6%
Other values (60) 15681
 
6.1%

Most occurring characters

ValueCountFrequency (%)
t 225147
19.3%
142357
12.2%
e 123085
10.6%
o 116327
10.0%
n 115742
9.9%
s 114708
9.8%
( 110059
9.4%
) 110059
9.4%
a 10384
 
0.9%
r 9241
 
0.8%
Other values (48) 88905
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1166014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 225147
19.3%
142357
12.2%
e 123085
10.6%
o 116327
10.0%
n 115742
9.9%
s 114708
9.8%
( 110059
9.4%
) 110059
9.4%
a 10384
 
0.9%
r 9241
 
0.8%
Other values (48) 88905
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1166014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 225147
19.3%
142357
12.2%
e 123085
10.6%
o 116327
10.0%
n 115742
9.9%
s 114708
9.8%
( 110059
9.4%
) 110059
9.4%
a 10384
 
0.9%
r 9241
 
0.8%
Other values (48) 88905
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1166014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 225147
19.3%
142357
12.2%
e 123085
10.6%
o 116327
10.0%
n 115742
9.9%
s 114708
9.8%
( 110059
9.4%
) 110059
9.4%
a 10384
 
0.9%
r 9241
 
0.8%
Other values (48) 88905
 
7.6%

device.mobileDeviceMarketingName
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:41.221445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:41.449903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

geoNetwork.networkDomain
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
domain1
38716 
domain2
38542 
domain3
38529 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters810509
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdomain1
2nd rowdomain3
3rd rowdomain1
4th rowdomain3
5th rowdomain1

Common Values

ValueCountFrequency (%)
domain1 38716
33.4%
domain2 38542
33.3%
domain3 38529
33.3%

Length

2025-07-17T15:30:41.628559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:42.046061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
domain1 38716
33.4%
domain2 38542
33.3%
domain3 38529
33.3%

Most occurring characters

ValueCountFrequency (%)
d 115787
14.3%
o 115787
14.3%
m 115787
14.3%
a 115787
14.3%
i 115787
14.3%
n 115787
14.3%
1 38716
 
4.8%
2 38542
 
4.8%
3 38529
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 810509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 115787
14.3%
o 115787
14.3%
m 115787
14.3%
a 115787
14.3%
i 115787
14.3%
n 115787
14.3%
1 38716
 
4.8%
2 38542
 
4.8%
3 38529
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 810509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 115787
14.3%
o 115787
14.3%
m 115787
14.3%
a 115787
14.3%
i 115787
14.3%
n 115787
14.3%
1 38716
 
4.8%
2 38542
 
4.8%
3 38529
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 810509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 115787
14.3%
o 115787
14.3%
m 115787
14.3%
a 115787
14.3%
i 115787
14.3%
n 115787
14.3%
1 38716
 
4.8%
2 38542
 
4.8%
3 38529
 
4.8%

gclIdPresent
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.3 MiB
0
111504 
1
 
4283

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters115787
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111504
96.3%
1 4283
 
3.7%

Length

2025-07-17T15:30:42.261600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:42.476393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 111504
96.3%
1 4283
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 111504
96.3%
1 4283
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111504
96.3%
1 4283
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111504
96.3%
1 4283
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111504
96.3%
1 4283
 
3.7%

device.operatingSystemVersion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:42.694964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:42.863940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

sessionNumber
Real number (ℝ)

Skewed 

Distinct230
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6918048
Minimum1
Maximum447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-07-17T15:30:43.100402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum447
Range446
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10.461609
Coefficient of variation (CV)3.8864664
Kurtosis555.38795
Mean2.6918048
Median Absolute Deviation (MAD)0
Skewness20.420319
Sum311676
Variance109.44526
MonotonicityNot monotonic
2025-07-17T15:30:43.353718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 80395
69.4%
2 14719
 
12.7%
3 6602
 
5.7%
4 3756
 
3.2%
5 2319
 
2.0%
6 1567
 
1.4%
7 1111
 
1.0%
8 791
 
0.7%
9 656
 
0.6%
10 490
 
0.4%
Other values (220) 3381
 
2.9%
ValueCountFrequency (%)
1 80395
69.4%
2 14719
 
12.7%
3 6602
 
5.7%
4 3756
 
3.2%
5 2319
 
2.0%
6 1567
 
1.4%
7 1111
 
1.0%
8 791
 
0.7%
9 656
 
0.6%
10 490
 
0.4%
ValueCountFrequency (%)
447 1
 
< 0.1%
430 1
 
< 0.1%
421 1
 
< 0.1%
412 1
 
< 0.1%
406 1
 
< 0.1%
396 1
 
< 0.1%
381 1
 
< 0.1%
378 3
< 0.1%
374 1
 
< 0.1%
365 1
 
< 0.1%

device.flashVersion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:43.652244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:43.843112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

geoNetwork.region
Text

Distinct388
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 MiB
2025-07-17T15:30:44.385390image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length33
Median length29
Mean length19.716523
Min length4

Characters and Unicode

Total characters2282917
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)0.1%

Sample

1st rowWashington
2nd rowCalifornia
3rd rowLombardy
4th rownot available in demo dataset
5th rownot available in demo dataset
ValueCountFrequency (%)
not 62825
16.6%
available 60021
15.9%
in 60021
15.9%
demo 60021
15.9%
dataset 60021
15.9%
california 18076
 
4.8%
new 6357
 
1.7%
york 5619
 
1.5%
set 2804
 
0.7%
illinois 1622
 
0.4%
Other values (461) 40922
10.8%
2025-07-17T15:30:45.200346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 371134
16.3%
262522
11.5%
e 204795
9.0%
t 201429
8.8%
i 182152
8.0%
o 166810
7.3%
n 162954
7.1%
l 151745
6.6%
d 125551
 
5.5%
s 76778
 
3.4%
Other values (46) 377047
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2282917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 371134
16.3%
262522
11.5%
e 204795
9.0%
t 201429
8.8%
i 182152
8.0%
o 166810
7.3%
n 162954
7.1%
l 151745
6.6%
d 125551
 
5.5%
s 76778
 
3.4%
Other values (46) 377047
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2282917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 371134
16.3%
262522
11.5%
e 204795
9.0%
t 201429
8.8%
i 182152
8.0%
o 166810
7.3%
n 162954
7.1%
l 151745
6.6%
d 125551
 
5.5%
s 76778
 
3.4%
Other values (46) 377047
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2282917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 371134
16.3%
262522
11.5%
e 204795
9.0%
t 201429
8.8%
i 182152
8.0%
o 166810
7.3%
n 162954
7.1%
l 151745
6.6%
d 125551
 
5.5%
s 76778
 
3.4%
Other values (46) 377047
16.5%

trafficSource
Text

Distinct161
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
2025-07-17T15:30:45.553006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length27
Median length25
Mean length8.70159
Min length3

Characters and Unicode

Total characters1007531
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)< 0.1%

Sample

1st rowyoutube.com
2nd row(direct)
3rd rowgoogle
4th rowyoutube.com
5th row(direct)
ValueCountFrequency (%)
google 44229
38.2%
direct 36741
31.7%
youtube.com 19195
16.6%
mall.googleplex.com 6388
 
5.5%
analytics.google.com 1826
 
1.6%
partners 1777
 
1.5%
dfa 611
 
0.5%
sites.google.com 533
 
0.5%
google.com 503
 
0.4%
m.facebook.com 365
 
0.3%
Other values (152) 3623
 
3.1%
2025-07-17T15:30:46.178135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 162980
16.2%
e 121196
12.0%
g 110062
10.9%
l 77276
 
7.7%
c 71380
 
7.1%
t 61399
 
6.1%
. 42559
 
4.2%
r 41500
 
4.1%
i 41293
 
4.1%
u 39837
 
4.0%
Other values (26) 238049
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1007531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 162980
16.2%
e 121196
12.0%
g 110062
10.9%
l 77276
 
7.7%
c 71380
 
7.1%
t 61399
 
6.1%
. 42559
 
4.2%
r 41500
 
4.1%
i 41293
 
4.1%
u 39837
 
4.0%
Other values (26) 238049
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1007531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 162980
16.2%
e 121196
12.0%
g 110062
10.9%
l 77276
 
7.7%
c 71380
 
7.1%
t 61399
 
6.1%
. 42559
 
4.2%
r 41500
 
4.1%
i 41293
 
4.1%
u 39837
 
4.0%
Other values (26) 238049
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1007531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 162980
16.2%
e 121196
12.0%
g 110062
10.9%
l 77276
 
7.7%
c 71380
 
7.1%
t 61399
 
6.1%
. 42559
 
4.2%
r 41500
 
4.1%
i 41293
 
4.1%
u 39837
 
4.0%
Other values (26) 238049
23.6%

totals.visits
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.3 MiB
1
115787 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters115787
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 115787
100.0%

Length

2025-07-17T15:30:46.512015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:46.690262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 115787
100.0%

Most occurring characters

ValueCountFrequency (%)
1 115787
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 115787
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 115787
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 115787
100.0%

geoNetwork.networkLocation
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:46.865033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:47.057505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

sessionId
Real number (ℝ)

High correlation 

Distinct107379
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4934412 × 109
Minimum1.4700355 × 109
Maximum1.5251568 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-07-17T15:30:47.349584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.4700355 × 109
5-th percentile1.4722335 × 109
Q11.480068 × 109
median1.4916578 × 109
Q31.5051385 × 109
95-th percentile1.5211249 × 109
Maximum1.5251568 × 109
Range55121338
Interquartile range (IQR)25070578

Descriptive statistics

Standard deviation15294977
Coefficient of variation (CV)0.010241432
Kurtosis-0.97608957
Mean1.4934412 × 109
Median Absolute Deviation (MAD)12110650
Skewness0.37103798
Sum1.7292108 × 1014
Variance2.3393631 × 1014
MonotonicityNot monotonic
2025-07-17T15:30:47.686520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1513124975 4
 
< 0.1%
1495198354 3
 
< 0.1%
1497588551 3
 
< 0.1%
1480130589 3
 
< 0.1%
1485213275 3
 
< 0.1%
1480384482 3
 
< 0.1%
1492636729 3
 
< 0.1%
1484515591 3
 
< 0.1%
1496416992 3
 
< 0.1%
1513124892 3
 
< 0.1%
Other values (107369) 115756
> 99.9%
ValueCountFrequency (%)
1470035457 1
< 0.1%
1470036782 1
< 0.1%
1470037302 1
< 0.1%
1470037559 1
< 0.1%
1470041199 1
< 0.1%
1470041583 1
< 0.1%
1470041675 1
< 0.1%
1470044563 1
< 0.1%
1470045423 1
< 0.1%
1470046282 1
< 0.1%
ValueCountFrequency (%)
1525156795 1
< 0.1%
1525155221 1
< 0.1%
1525153543 1
< 0.1%
1525151200 1
< 0.1%
1525149884 1
< 0.1%
1525149778 1
< 0.1%
1525149427 1
< 0.1%
1525148316 1
< 0.1%
1525147832 1
< 0.1%
1525147674 1
< 0.1%

os
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
Windows
39069 
Macintosh
37556 
Android
15849 
iOS
12742 
Linux
5064 
Other values (13)
5507 

Length

Max length16
Median length13
Mean length7.2181765
Min length3

Characters and Unicode

Total characters835771
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowWindows
2nd rowMacintosh
3rd rowWindows
4th rowWindows
5th rowWindows

Common Values

ValueCountFrequency (%)
Windows 39069
33.7%
Macintosh 37556
32.4%
Android 15849
13.7%
iOS 12742
 
11.0%
Linux 5064
 
4.4%
Chrome OS 4724
 
4.1%
(not set) 565
 
0.5%
Windows Phone 95
 
0.1%
Samsung 46
 
< 0.1%
Tizen 22
 
< 0.1%
Other values (8) 55
 
< 0.1%

Length

2025-07-17T15:30:47.939036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
windows 39164
32.3%
macintosh 37556
31.0%
android 15849
13.1%
ios 12742
 
10.5%
linux 5064
 
4.2%
os 4725
 
3.9%
chrome 4724
 
3.9%
not 565
 
0.5%
set 565
 
0.5%
phone 95
 
0.1%
Other values (12) 145
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 110462
13.2%
n 98403
11.8%
o 97985
11.7%
s 77333
9.3%
d 70882
 
8.5%
h 42375
 
5.1%
W 39184
 
4.7%
w 39164
 
4.7%
t 38712
 
4.6%
a 37626
 
4.5%
Other values (33) 183645
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 835771
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 110462
13.2%
n 98403
11.8%
o 97985
11.7%
s 77333
9.3%
d 70882
 
8.5%
h 42375
 
5.1%
W 39184
 
4.7%
w 39164
 
4.7%
t 38712
 
4.6%
a 37626
 
4.5%
Other values (33) 183645
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 835771
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 110462
13.2%
n 98403
11.8%
o 97985
11.7%
s 77333
9.3%
d 70882
 
8.5%
h 42375
 
5.1%
W 39184
 
4.7%
w 39164
 
4.7%
t 38712
 
4.6%
a 37626
 
4.5%
Other values (33) 183645
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 835771
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 110462
13.2%
n 98403
11.8%
o 97985
11.7%
s 77333
9.3%
d 70882
 
8.5%
h 42375
 
5.1%
W 39184
 
4.7%
w 39164
 
4.7%
t 38712
 
4.6%
a 37626
 
4.5%
Other values (33) 183645
22.0%

geoNetwork.subContinent
Categorical

High correlation 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
Northern America
63397 
Southeast Asia
7143 
Southern Asia
6482 
Western Europe
 
6262
Northern Europe
 
5996
Other values (18)
26507 

Length

Max length18
Median length16
Mean length14.901612
Min length9

Characters and Unicode

Total characters1725413
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthern America
2nd rowNorthern America
3rd rowSouthern Europe
4th rowEastern Asia
5th rowNorthern America

Common Values

ValueCountFrequency (%)
Northern America 63397
54.8%
Southeast Asia 7143
 
6.2%
Southern Asia 6482
 
5.6%
Western Europe 6262
 
5.4%
Northern Europe 5996
 
5.2%
Eastern Asia 5035
 
4.3%
South America 4328
 
3.7%
Eastern Europe 4275
 
3.7%
Southern Europe 3754
 
3.2%
Western Asia 3578
 
3.1%
Other values (13) 5537
 
4.8%

Length

2025-07-17T15:30:48.181629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
northern 70321
30.6%
america 69398
30.2%
asia 22370
 
9.7%
europe 20287
 
8.8%
southern 10500
 
4.6%
western 10154
 
4.4%
eastern 9503
 
4.1%
southeast 7143
 
3.1%
south 4328
 
1.9%
central 1805
 
0.8%
Other values (10) 3924
 
1.7%

Most occurring characters

ValueCountFrequency (%)
r 265878
15.4%
e 209728
12.2%
t 122755
 
7.1%
a 117240
 
6.8%
113946
 
6.6%
o 112739
 
6.5%
n 102707
 
6.0%
i 95431
 
5.5%
A 95098
 
5.5%
h 92292
 
5.3%
Other values (21) 397599
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1725413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 265878
15.4%
e 209728
12.2%
t 122755
 
7.1%
a 117240
 
6.8%
113946
 
6.6%
o 112739
 
6.5%
n 102707
 
6.0%
i 95431
 
5.5%
A 95098
 
5.5%
h 92292
 
5.3%
Other values (21) 397599
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1725413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 265878
15.4%
e 209728
12.2%
t 122755
 
7.1%
a 117240
 
6.8%
113946
 
6.6%
o 112739
 
6.5%
n 102707
 
6.0%
i 95431
 
5.5%
A 95098
 
5.5%
h 92292
 
5.3%
Other values (21) 397599
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1725413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 265878
15.4%
e 209728
12.2%
t 122755
 
7.1%
a 117240
 
6.8%
113946
 
6.6%
o 112739
 
6.5%
n 102707
 
6.0%
i 95431
 
5.5%
A 95098
 
5.5%
h 92292
 
5.3%
Other values (21) 397599
23.0%

trafficSource.medium
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
organic
40561 
(none)
36741 
referral
31631 
cpc
4274 
affiliate
 
1775
Other values (2)
 
805

Length

Max length9
Median length8
Mean length6.8115851
Min length3

Characters and Unicode

Total characters788693
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreferral
2nd row(none)
3rd roworganic
4th rowreferral
5th row(none)

Common Values

ValueCountFrequency (%)
organic 40561
35.0%
(none) 36741
31.7%
referral 31631
27.3%
cpc 4274
 
3.7%
affiliate 1775
 
1.5%
cpm 795
 
0.7%
(not set) 10
 
< 0.1%

Length

2025-07-17T15:30:48.491738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:48.857776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
organic 40561
35.0%
none 36741
31.7%
referral 31631
27.3%
cpc 4274
 
3.7%
affiliate 1775
 
1.5%
cpm 795
 
0.7%
not 10
 
< 0.1%
set 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 135454
17.2%
n 114053
14.5%
e 101788
12.9%
o 77312
9.8%
a 75742
9.6%
c 49904
 
6.3%
i 44111
 
5.6%
g 40561
 
5.1%
( 36751
 
4.7%
) 36751
 
4.7%
Other values (7) 76266
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 788693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 135454
17.2%
n 114053
14.5%
e 101788
12.9%
o 77312
9.8%
a 75742
9.6%
c 49904
 
6.3%
i 44111
 
5.6%
g 40561
 
5.1%
( 36751
 
4.7%
) 36751
 
4.7%
Other values (7) 76266
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 788693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 135454
17.2%
n 114053
14.5%
e 101788
12.9%
o 77312
9.8%
a 75742
9.6%
c 49904
 
6.3%
i 44111
 
5.6%
g 40561
 
5.1%
( 36751
 
4.7%
) 36751
 
4.7%
Other values (7) 76266
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 788693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 135454
17.2%
n 114053
14.5%
e 101788
12.9%
o 77312
9.8%
a 75742
9.6%
c 49904
 
6.3%
i 44111
 
5.6%
g 40561
 
5.1%
( 36751
 
4.7%
) 36751
 
4.7%
Other values (7) 76266
9.7%

trafficSource.adwordsClickInfo.isVideoAd
Boolean

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing111518
Missing (%)96.3%
Memory size4.4 MiB
False
 
4269
(Missing)
111518 
ValueCountFrequency (%)
False 4269
 
3.7%
(Missing) 111518
96.3%
2025-07-17T15:30:49.062024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

browserMajor
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:49.316919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:49.521851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

locationCountry
Text

Distinct193
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.3 MiB
2025-07-17T15:30:49.887452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length24
Median length13
Mean length10.34921
Min length4

Characters and Unicode

Total characters1198304
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowItaly
4th rowJapan
5th rowUnited States
ValueCountFrequency (%)
united 64373
35.1%
states 60178
32.8%
india 5594
 
3.1%
kingdom 3902
 
2.1%
canada 3218
 
1.8%
vietnam 2116
 
1.2%
japan 2031
 
1.1%
germany 2024
 
1.1%
brazil 1979
 
1.1%
turkey 1819
 
1.0%
Other values (214) 36131
19.7%
2025-07-17T15:30:50.747118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 195386
16.3%
e 150111
12.5%
a 122986
10.3%
n 104469
8.7%
i 100755
8.4%
d 85225
7.1%
s 70067
 
5.8%
67578
 
5.6%
S 65141
 
5.4%
U 65047
 
5.4%
Other values (52) 171539
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1198304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 195386
16.3%
e 150111
12.5%
a 122986
10.3%
n 104469
8.7%
i 100755
8.4%
d 85225
7.1%
s 70067
 
5.8%
67578
 
5.6%
S 65141
 
5.4%
U 65047
 
5.4%
Other values (52) 171539
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1198304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 195386
16.3%
e 150111
12.5%
a 122986
10.3%
n 104469
8.7%
i 100755
8.4%
d 85225
7.1%
s 70067
 
5.8%
67578
 
5.6%
S 65141
 
5.4%
U 65047
 
5.4%
Other values (52) 171539
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1198304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 195386
16.3%
e 150111
12.5%
a 122986
10.3%
n 104469
8.7%
i 100755
8.4%
d 85225
7.1%
s 70067
 
5.8%
67578
 
5.6%
S 65141
 
5.4%
U 65047
 
5.4%
Other values (52) 171539
14.3%

device.browserSize
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:51.061669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:51.244219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

trafficSource.adwordsClickInfo.adNetworkType
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing111518
Missing (%)96.3%
Memory size5.4 MiB
Google Search
2772 
Content
1497 

Length

Max length13
Median length13
Mean length10.895994
Min length7

Characters and Unicode

Total characters46515
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGoogle Search
2nd rowGoogle Search
3rd rowGoogle Search
4th rowGoogle Search
5th rowGoogle Search

Common Values

ValueCountFrequency (%)
Google Search 2772
 
2.4%
Content 1497
 
1.3%
(Missing) 111518
96.3%

Length

2025-07-17T15:30:51.484153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:51.741791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
google 2772
39.4%
search 2772
39.4%
content 1497
21.3%

Most occurring characters

ValueCountFrequency (%)
o 7041
15.1%
e 7041
15.1%
n 2994
 
6.4%
t 2994
 
6.4%
G 2772
 
6.0%
g 2772
 
6.0%
l 2772
 
6.0%
2772
 
6.0%
S 2772
 
6.0%
a 2772
 
6.0%
Other values (4) 9813
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 7041
15.1%
e 7041
15.1%
n 2994
 
6.4%
t 2994
 
6.4%
G 2772
 
6.0%
g 2772
 
6.0%
l 2772
 
6.0%
2772
 
6.0%
S 2772
 
6.0%
a 2772
 
6.0%
Other values (4) 9813
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 7041
15.1%
e 7041
15.1%
n 2994
 
6.4%
t 2994
 
6.4%
G 2772
 
6.0%
g 2772
 
6.0%
l 2772
 
6.0%
2772
 
6.0%
S 2772
 
6.0%
a 2772
 
6.0%
Other values (4) 9813
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 7041
15.1%
e 7041
15.1%
n 2994
 
6.4%
t 2994
 
6.4%
G 2772
 
6.0%
g 2772
 
6.0%
l 2772
 
6.0%
2772
 
6.0%
S 2772
 
6.0%
a 2772
 
6.0%
Other values (4) 9813
21.1%

socialEngagementType
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 MiB
Not Socially Engaged
115787 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters2315740
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Socially Engaged
2nd rowNot Socially Engaged
3rd rowNot Socially Engaged
4th rowNot Socially Engaged
5th rowNot Socially Engaged

Common Values

ValueCountFrequency (%)
Not Socially Engaged 115787
100.0%

Length

2025-07-17T15:30:51.967842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:52.114657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
33.3%
socially 115787
33.3%
engaged 115787
33.3%

Most occurring characters

ValueCountFrequency (%)
o 231574
 
10.0%
231574
 
10.0%
a 231574
 
10.0%
l 231574
 
10.0%
g 231574
 
10.0%
N 115787
 
5.0%
t 115787
 
5.0%
S 115787
 
5.0%
c 115787
 
5.0%
i 115787
 
5.0%
Other values (5) 578935
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2315740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 231574
 
10.0%
231574
 
10.0%
a 231574
 
10.0%
l 231574
 
10.0%
g 231574
 
10.0%
N 115787
 
5.0%
t 115787
 
5.0%
S 115787
 
5.0%
c 115787
 
5.0%
i 115787
 
5.0%
Other values (5) 578935
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2315740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 231574
 
10.0%
231574
 
10.0%
a 231574
 
10.0%
l 231574
 
10.0%
g 231574
 
10.0%
N 115787
 
5.0%
t 115787
 
5.0%
S 115787
 
5.0%
c 115787
 
5.0%
i 115787
 
5.0%
Other values (5) 578935
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2315740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 231574
 
10.0%
231574
 
10.0%
a 231574
 
10.0%
l 231574
 
10.0%
g 231574
 
10.0%
N 115787
 
5.0%
t 115787
 
5.0%
S 115787
 
5.0%
c 115787
 
5.0%
i 115787
 
5.0%
Other values (5) 578935
25.0%

geoNetwork.city
Text

Distinct695
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
2025-07-17T15:30:52.800093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length33
Median length29
Mean length19.373237
Min length3

Characters and Unicode

Total characters2243169
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)0.1%

Sample

1st rowRedmond
2nd rowMountain View
3rd rowMilan
4th rownot available in demo dataset
5th rownot available in demo dataset
ValueCountFrequency (%)
not 63621
16.5%
demo 60021
15.5%
dataset 60021
15.5%
available 60021
15.5%
in 60021
15.5%
view 6655
 
1.7%
mountain 6655
 
1.7%
new 5967
 
1.5%
san 5807
 
1.5%
york 5607
 
1.5%
Other values (777) 51976
13.5%
2025-07-17T15:30:53.730692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 344318
15.3%
270585
12.1%
e 216615
9.7%
t 206748
9.2%
n 173239
7.7%
o 164092
7.3%
i 153090
6.8%
l 135185
 
6.0%
d 126323
 
5.6%
s 77813
 
3.5%
Other values (48) 375161
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2243169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 344318
15.3%
270585
12.1%
e 216615
9.7%
t 206748
9.2%
n 173239
7.7%
o 164092
7.3%
i 153090
6.8%
l 135185
 
6.0%
d 126323
 
5.6%
s 77813
 
3.5%
Other values (48) 375161
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2243169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 344318
15.3%
270585
12.1%
e 216615
9.7%
t 206748
9.2%
n 173239
7.7%
o 164092
7.3%
i 153090
6.8%
l 135185
 
6.0%
d 126323
 
5.6%
s 77813
 
3.5%
Other values (48) 375161
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2243169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 344318
15.3%
270585
12.1%
e 216615
9.7%
t 206748
9.2%
n 173239
7.7%
o 164092
7.3%
i 153090
6.8%
l 135185
 
6.0%
d 126323
 
5.6%
s 77813
 
3.5%
Other values (48) 375161
16.7%

trafficSource.adwordsClickInfo.page
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.1%
Missing111518
Missing (%)96.3%
Memory size5.4 MiB
1.0
4204 
2.0
 
50
3.0
 
12
4.0
 
2
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12807
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4204
 
3.6%
2.0 50
 
< 0.1%
3.0 12
 
< 0.1%
4.0 2
 
< 0.1%
5.0 1
 
< 0.1%
(Missing) 111518
96.3%

Length

2025-07-17T15:30:54.033813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:54.224052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4204
98.5%
2.0 50
 
1.2%
3.0 12
 
0.3%
4.0 2
 
< 0.1%
5.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 4269
33.3%
0 4269
33.3%
1 4204
32.8%
2 50
 
0.4%
3 12
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4269
33.3%
0 4269
33.3%
1 4204
32.8%
2 50
 
0.4%
3 12
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4269
33.3%
0 4269
33.3%
1 4204
32.8%
2 50
 
0.4%
3 12
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4269
33.3%
0 4269
33.3%
1 4204
32.8%
2 50
 
0.4%
3 12
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

geoNetwork.metro
Text

Distinct104
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2025-07-17T15:30:54.693366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length41
Median length29
Mean length23.276015
Min length6

Characters and Unicode

Total characters2695060
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowSeattle-Tacoma WA
2nd rowSan Francisco-Oakland-San Jose CA
3rd row(not set)
4th rownot available in demo dataset
5th rownot available in demo dataset
ValueCountFrequency (%)
not 81642
18.0%
in 60041
13.3%
demo 60021
13.3%
dataset 60021
13.3%
available 60021
13.3%
set 21621
 
4.8%
ca 18062
 
4.0%
san 16376
 
3.6%
francisco-oakland-san 16027
 
3.5%
jose 16027
 
3.5%
Other values (174) 42566
9.4%
2025-07-17T15:30:55.320559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 392276
14.6%
336638
12.5%
e 234597
8.7%
t 234036
8.7%
n 216945
 
8.0%
o 192953
 
7.2%
l 141879
 
5.3%
i 141265
 
5.2%
d 138030
 
5.1%
s 121791
 
4.5%
Other values (47) 544650
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2695060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 392276
14.6%
336638
12.5%
e 234597
8.7%
t 234036
8.7%
n 216945
 
8.0%
o 192953
 
7.2%
l 141879
 
5.3%
i 141265
 
5.2%
d 138030
 
5.1%
s 121791
 
4.5%
Other values (47) 544650
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2695060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 392276
14.6%
336638
12.5%
e 234597
8.7%
t 234036
8.7%
n 216945
 
8.0%
o 192953
 
7.2%
l 141879
 
5.3%
i 141265
 
5.2%
d 138030
 
5.1%
s 121791
 
4.5%
Other values (47) 544650
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2695060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 392276
14.6%
336638
12.5%
e 234597
8.7%
t 234036
8.7%
n 216945
 
8.0%
o 192953
 
7.2%
l 141879
 
5.3%
i 141265
 
5.2%
d 138030
 
5.1%
s 121791
 
4.5%
Other values (47) 544650
20.2%

pageViews
Real number (ℝ)

High correlation 

Distinct175
Distinct (%)0.2%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8.3813299
Minimum1
Maximum469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-07-17T15:30:55.528087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q310
95-th percentile35
Maximum469
Range468
Interquartile range (IQR)9

Descriptive statistics

Standard deviation14.299764
Coefficient of variation (CV)1.7061449
Kurtosis91.718942
Mean8.3813299
Median Absolute Deviation (MAD)1
Skewness5.7896398
Sum970382
Variance204.48324
MonotonicityNot monotonic
2025-07-17T15:30:55.761689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47345
40.9%
2 14145
 
12.2%
3 7622
 
6.6%
4 4770
 
4.1%
5 3508
 
3.0%
6 2664
 
2.3%
7 2079
 
1.8%
8 1843
 
1.6%
11 1644
 
1.4%
9 1637
 
1.4%
Other values (165) 28522
24.6%
ValueCountFrequency (%)
1 47345
40.9%
2 14145
 
12.2%
3 7622
 
6.6%
4 4770
 
4.1%
5 3508
 
3.0%
6 2664
 
2.3%
7 2079
 
1.8%
8 1843
 
1.6%
9 1637
 
1.4%
10 1628
 
1.4%
ValueCountFrequency (%)
469 2
< 0.1%
466 1
< 0.1%
431 2
< 0.1%
400 2
< 0.1%
351 1
< 0.1%
343 2
< 0.1%
341 2
< 0.1%
323 1
< 0.1%
309 2
< 0.1%
305 2
< 0.1%

locationZone
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.3 MiB
8
115787 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters115787
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 115787
100.0%

Length

2025-07-17T15:30:55.988654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:56.165888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
8 115787
100.0%

Most occurring characters

ValueCountFrequency (%)
8 115787
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 115787
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 115787
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 115787
100.0%

device.mobileDeviceModel
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:56.382419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:56.543421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

trafficSource.referralPath
Path

Missing 

Distinct941
Distinct (%)2.2%
Missing73099
Missing (%)63.1%
Memory size5.9 MiB
/
17967 
/yt/about/
5209 
/analytics/web/
 
1633
/yt/about/tr/
 
1053
/yt/about/vi/
 
967
Other values (936)
15859 

Length

Max length270
Median length198
Mean length10.904704
Min length1

Characters and Unicode

Total characters465500
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique406 ?
Unique (%)1.0%

Sample

1st row/intl/hr/yt/about/
2nd row/yt/about/ja/
3rd row/
4th row/
5th row/yt/about/pt-BR/

Common Values

ValueCountFrequency (%)
/ 17967
 
15.5%
/yt/about/ 5209
 
4.5%
/analytics/web/ 1633
 
1.4%
/yt/about/tr/ 1053
 
0.9%
/yt/about/vi/ 967
 
0.8%
/yt/about/es-419/ 883
 
0.8%
/yt/about/pt-BR/ 825
 
0.7%
/yt/about/ru/ 800
 
0.7%
/yt/about/th/ 799
 
0.7%
/yt/about/en-GB/ 491
 
0.4%
Other values (931) 12061
 
10.4%
(Missing) 73099
63.1%

Length

2025-07-17T15:30:56.786677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17967
42.1%
yt/about 5209
 
12.2%
analytics/web 1633
 
3.8%
yt/about/tr 1053
 
2.5%
yt/about/vi 967
 
2.3%
yt/about/es-419 883
 
2.1%
yt/about/pt-br 825
 
1.9%
yt/about/ru 800
 
1.9%
yt/about/th 799
 
1.9%
yt/about/en-gb 491
 
1.2%
Other values (928) 12061
28.3%

Most occurring characters

ValueCountFrequency (%)
/ 108184
23.2%
t 52406
11.3%
o 31913
 
6.9%
a 30455
 
6.5%
y 22037
 
4.7%
u 21992
 
4.7%
b 20470
 
4.4%
e 20163
 
4.3%
i 15792
 
3.4%
s 15189
 
3.3%
Other values (62) 126899
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 465500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 108184
23.2%
t 52406
11.3%
o 31913
 
6.9%
a 30455
 
6.5%
y 22037
 
4.7%
u 21992
 
4.7%
b 20470
 
4.4%
e 20163
 
4.3%
i 15792
 
3.4%
s 15189
 
3.3%
Other values (62) 126899
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 465500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 108184
23.2%
t 52406
11.3%
o 31913
 
6.9%
a 30455
 
6.5%
y 22037
 
4.7%
u 21992
 
4.7%
b 20470
 
4.4%
e 20163
 
4.3%
i 15792
 
3.4%
s 15189
 
3.3%
Other values (62) 126899
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 465500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 108184
23.2%
t 52406
11.3%
o 31913
 
6.9%
a 30455
 
6.5%
y 22037
 
4.7%
u 21992
 
4.7%
b 20470
 
4.4%
e 20163
 
4.3%
i 15792
 
3.4%
s 15189
 
3.3%
Other values (62) 126899
27.3%
Common prefix/
Unique stems941
Unique names209
Unique extensions12
Unique directories744
Unique anchors1
ValueCountFrequency (%)
/ 17967
 
15.5%
/yt/about/ 5209
 
4.5%
/analytics/web/ 1633
 
1.4%
/yt/about/tr/ 1053
 
0.9%
/yt/about/vi/ 967
 
0.8%
/yt/about/es-419/ 883
 
0.8%
/yt/about/pt-BR/ 825
 
0.7%
/yt/about/ru/ 800
 
0.7%
/yt/about/th/ 799
 
0.7%
/yt/about/en-GB/ 491
 
0.4%
Other values (931) 12061
 
10.4%
(Missing) 73099
63.1%
ValueCountFrequency (%)
/ 17967
 
15.5%
/yt/about/ 5209
 
4.5%
/analytics/web/ 1633
 
1.4%
/yt/about/tr/ 1053
 
0.9%
/yt/about/vi/ 967
 
0.8%
/yt/about/es-419/ 883
 
0.8%
/yt/about/pt-BR/ 825
 
0.7%
/yt/about/ru/ 800
 
0.7%
/yt/about/th/ 799
 
0.7%
/yt/about/en-GB/ 491
 
0.4%
Other values (931) 12061
 
10.4%
(Missing) 73099
63.1%
ValueCountFrequency (%)
38927
33.6%
using-the-logo.html 558
 
0.5%
index.html 434
 
0.4%
2145 428
 
0.4%
alphabet-google-discounts 311
 
0.3%
c10b14f9a69ff71b1b7a 187
 
0.2%
How-To-Visit-the-Googleplex-the-Google-Head-Office-in-Mountain-View.htm 147
 
0.1%
ads 138
 
0.1%
inpage_launch 102
 
0.1%
mobile 80
 
0.1%
Other values (199) 1376
 
1.2%
(Missing) 73099
63.1%
ValueCountFrequency (%)
41193
35.6%
.html 1176
 
1.0%
.htm 147
 
0.1%
.php 85
 
0.1%
.aspx 33
 
< 0.1%
.jhtml 19
 
< 0.1%
.com 13
 
< 0.1%
.jspa 11
 
< 0.1%
.jsp 8
 
< 0.1%
.75j66c1p61j36b9ocgp30b9k6dh3gb9pchhm2bb36tgj0cj56hij6d35c8 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 73099
63.1%
ValueCountFrequency (%)
/ 18783
 
16.2%
/yt/about 5231
 
4.5%
/analytics/web 1735
 
1.5%
/yt/about/tr 1058
 
0.9%
/yt/about/vi 967
 
0.8%
/yt/about/es-419 889
 
0.8%
/yt/about/pt-BR 830
 
0.7%
/yt/about/ru 806
 
0.7%
/yt/about/th 803
 
0.7%
/yt/about/en-GB 495
 
0.4%
Other values (734) 11091
 
9.6%
(Missing) 73099
63.1%
ValueCountFrequency (%)
42688
36.9%
(Missing) 73099
63.1%

totals.bounces
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing68666
Missing (%)59.3%
Memory size6.2 MiB
1.0
47121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters141363
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 47121
40.7%
(Missing) 68666
59.3%

Length

2025-07-17T15:30:56.988492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:57.109051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 47121
100.0%

Most occurring characters

ValueCountFrequency (%)
1 47121
33.3%
. 47121
33.3%
0 47121
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 47121
33.3%
. 47121
33.3%
0 47121
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 47121
33.3%
. 47121
33.3%
0 47121
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 47121
33.3%
. 47121
33.3%
0 47121
33.3%

date
Date

Distinct638
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-08-01 00:00:00
Maximum2018-04-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-17T15:30:57.277407image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:57.489306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

device.language
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:57.836466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:58.049621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

deviceType
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
desktop
86281 
mobile
25818 
tablet
 
3688

Length

Max length7
Median length7
Mean length6.74517
Min length6

Characters and Unicode

Total characters781003
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdesktop
2nd rowdesktop
3rd rowdesktop
4th rowdesktop
5th rowdesktop

Common Values

ValueCountFrequency (%)
desktop 86281
74.5%
mobile 25818
 
22.3%
tablet 3688
 
3.2%

Length

2025-07-17T15:30:58.224136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:58.421041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
desktop 86281
74.5%
mobile 25818
 
22.3%
tablet 3688
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 115787
14.8%
o 112099
14.4%
t 93657
12.0%
d 86281
11.0%
s 86281
11.0%
k 86281
11.0%
p 86281
11.0%
b 29506
 
3.8%
l 29506
 
3.8%
m 25818
 
3.3%
Other values (2) 29506
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 781003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 115787
14.8%
o 112099
14.4%
t 93657
12.0%
d 86281
11.0%
s 86281
11.0%
k 86281
11.0%
p 86281
11.0%
b 29506
 
3.8%
l 29506
 
3.8%
m 25818
 
3.3%
Other values (2) 29506
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 781003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 115787
14.8%
o 112099
14.4%
t 93657
12.0%
d 86281
11.0%
s 86281
11.0%
k 86281
11.0%
p 86281
11.0%
b 29506
 
3.8%
l 29506
 
3.8%
m 25818
 
3.3%
Other values (2) 29506
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 781003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 115787
14.8%
o 112099
14.4%
t 93657
12.0%
d 86281
11.0%
s 86281
11.0%
k 86281
11.0%
p 86281
11.0%
b 29506
 
3.8%
l 29506
 
3.8%
m 25818
 
3.3%
Other values (2) 29506
 
3.8%

userChannel
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.3 MiB
Organic Search
46832 
Referral
21929 
Social
20763 
Direct
18790 
Paid Search
 
3394
Other values (3)
 
4079

Length

Max length14
Median length11
Mean length9.8422966
Min length6

Characters and Unicode

Total characters1139610
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSocial
2nd rowDirect
3rd rowOrganic Search
4th rowSocial
5th rowDirect

Common Values

ValueCountFrequency (%)
Organic Search 46832
40.4%
Referral 21929
18.9%
Social 20763
17.9%
Direct 18790
16.2%
Paid Search 3394
 
2.9%
Display 2294
 
2.0%
Affiliates 1775
 
1.5%
(Other) 10
 
< 0.1%

Length

2025-07-17T15:30:58.690653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:58.913493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
search 50226
30.3%
organic 46832
28.2%
referral 21929
13.2%
social 20763
12.5%
direct 18790
 
11.3%
paid 3394
 
2.0%
display 2294
 
1.4%
affiliates 1775
 
1.1%
other 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 159716
14.0%
a 147213
12.9%
c 136611
12.0%
e 114659
10.1%
i 95623
8.4%
S 70989
 
6.2%
h 50236
 
4.4%
50226
 
4.4%
O 46842
 
4.1%
g 46832
 
4.1%
Other values (15) 220663
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1139610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 159716
14.0%
a 147213
12.9%
c 136611
12.0%
e 114659
10.1%
i 95623
8.4%
S 70989
 
6.2%
h 50236
 
4.4%
50226
 
4.4%
O 46842
 
4.1%
g 46832
 
4.1%
Other values (15) 220663
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1139610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 159716
14.0%
a 147213
12.9%
c 136611
12.0%
e 114659
10.1%
i 95623
8.4%
S 70989
 
6.2%
h 50236
 
4.4%
50226
 
4.4%
O 46842
 
4.1%
g 46832
 
4.1%
Other values (15) 220663
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1139610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 159716
14.0%
a 147213
12.9%
c 136611
12.0%
e 114659
10.1%
i 95623
8.4%
S 70989
 
6.2%
h 50236
 
4.4%
50226
 
4.4%
O 46842
 
4.1%
g 46832
 
4.1%
Other values (15) 220663
19.4%

device.browserVersion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:30:59.202010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:30:59.414856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

totalHits
Real number (ℝ)

High correlation 

Distinct225
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.565936
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-07-17T15:30:59.620980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q312
95-th percentile46
Maximum500
Range499
Interquartile range (IQR)11

Descriptive statistics

Standard deviation19.55884
Coefficient of variation (CV)1.8511223
Kurtosis71.630015
Mean10.565936
Median Absolute Deviation (MAD)1
Skewness5.6043409
Sum1223398
Variance382.54821
MonotonicityNot monotonic
2025-07-17T15:30:59.904770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 46708
40.3%
2 13511
 
11.7%
3 7188
 
6.2%
4 4422
 
3.8%
5 3410
 
2.9%
6 2582
 
2.2%
7 2086
 
1.8%
8 1763
 
1.5%
9 1536
 
1.3%
13 1379
 
1.2%
Other values (215) 31202
26.9%
ValueCountFrequency (%)
1 46708
40.3%
2 13511
 
11.7%
3 7188
 
6.2%
4 4422
 
3.8%
5 3410
 
2.9%
6 2582
 
2.2%
7 2086
 
1.8%
8 1763
 
1.5%
9 1536
 
1.3%
10 1330
 
1.1%
ValueCountFrequency (%)
500 9
< 0.1%
471 2
 
< 0.1%
427 1
 
< 0.1%
387 2
 
< 0.1%
386 2
 
< 0.1%
385 3
 
< 0.1%
382 1
 
< 0.1%
378 1
 
< 0.1%
361 2
 
< 0.1%
347 2
 
< 0.1%

device.screenColors
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
not available in demo dataset
115787 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters3357823
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot available in demo dataset
2nd rownot available in demo dataset
3rd rownot available in demo dataset
4th rownot available in demo dataset
5th rownot available in demo dataset

Common Values

ValueCountFrequency (%)
not available in demo dataset 115787
100.0%

Length

2025-07-17T15:31:00.133000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:31:00.309844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
not 115787
20.0%
available 115787
20.0%
in 115787
20.0%
demo 115787
20.0%
dataset 115787
20.0%

Most occurring characters

ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 578935
17.2%
463148
13.8%
t 347361
10.3%
e 347361
10.3%
n 231574
 
6.9%
o 231574
 
6.9%
i 231574
 
6.9%
l 231574
 
6.9%
d 231574
 
6.9%
v 115787
 
3.4%
Other values (3) 347361
10.3%

sessionStart
Real number (ℝ)

High correlation 

Distinct107388
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4934412 × 109
Minimum1.4700355 × 109
Maximum1.5251568 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-07-17T15:31:00.643183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.4700355 × 109
5-th percentile1.4722335 × 109
Q11.480068 × 109
median1.4916578 × 109
Q31.5051385 × 109
95-th percentile1.5211249 × 109
Maximum1.5251568 × 109
Range55121338
Interquartile range (IQR)25070578

Descriptive statistics

Standard deviation15294977
Coefficient of variation (CV)0.010241432
Kurtosis-0.97608962
Mean1.4934412 × 109
Median Absolute Deviation (MAD)12110650
Skewness0.371038
Sum1.7292108 × 1014
Variance2.3393631 × 1014
MonotonicityNot monotonic
2025-07-17T15:31:00.944781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1513124975 4
 
< 0.1%
1486137202 4
 
< 0.1%
1471935751 3
 
< 0.1%
1500613740 3
 
< 0.1%
1513125075 3
 
< 0.1%
1480384482 3
 
< 0.1%
1495198354 3
 
< 0.1%
1496416992 3
 
< 0.1%
1490815247 3
 
< 0.1%
1513124892 3
 
< 0.1%
Other values (107378) 115755
> 99.9%
ValueCountFrequency (%)
1470035457 1
< 0.1%
1470036782 1
< 0.1%
1470037302 1
< 0.1%
1470037559 1
< 0.1%
1470041199 1
< 0.1%
1470041583 1
< 0.1%
1470041675 1
< 0.1%
1470044563 1
< 0.1%
1470045423 1
< 0.1%
1470046282 1
< 0.1%
ValueCountFrequency (%)
1525156795 1
< 0.1%
1525155221 1
< 0.1%
1525153543 1
< 0.1%
1525151200 1
< 0.1%
1525149884 1
< 0.1%
1525149778 1
< 0.1%
1525149427 1
< 0.1%
1525148316 1
< 0.1%
1525147832 1
< 0.1%
1525147674 1
< 0.1%

geoNetwork.continent
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
Americas
69646 
Asia
22370 
Europe
20287 
Africa
 
1744
Oceania
 
1604

Length

Max length9
Median length8
Mean length6.8339796
Min length4

Characters and Unicode

Total characters791286
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmericas
2nd rowAmericas
3rd rowEurope
4th rowAsia
5th rowAmericas

Common Values

ValueCountFrequency (%)
Americas 69646
60.2%
Asia 22370
 
19.3%
Europe 20287
 
17.5%
Africa 1744
 
1.5%
Oceania 1604
 
1.4%
(not set) 136
 
0.1%

Length

2025-07-17T15:31:01.199363image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:31:01.407110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
americas 69646
60.1%
asia 22370
 
19.3%
europe 20287
 
17.5%
africa 1744
 
1.5%
oceania 1604
 
1.4%
not 136
 
0.1%
set 136
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 96968
12.3%
i 95364
12.1%
A 93760
11.8%
s 92152
11.6%
r 91677
11.6%
e 91673
11.6%
c 72994
9.2%
m 69646
8.8%
o 20423
 
2.6%
p 20287
 
2.6%
Other values (9) 46342
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 791286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 96968
12.3%
i 95364
12.1%
A 93760
11.8%
s 92152
11.6%
r 91677
11.6%
e 91673
11.6%
c 72994
9.2%
m 69646
8.8%
o 20423
 
2.6%
p 20287
 
2.6%
Other values (9) 46342
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 791286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 96968
12.3%
i 95364
12.1%
A 93760
11.8%
s 92152
11.6%
r 91677
11.6%
e 91673
11.6%
c 72994
9.2%
m 69646
8.8%
o 20423
 
2.6%
p 20287
 
2.6%
Other values (9) 46342
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 791286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 96968
12.3%
i 95364
12.1%
A 93760
11.8%
s 92152
11.6%
r 91677
11.6%
e 91673
11.6%
c 72994
9.2%
m 69646
8.8%
o 20423
 
2.6%
p 20287
 
2.6%
Other values (9) 46342
5.9%

device.isMobile
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1017.7 KiB
False
86295 
True
29492 
ValueCountFrequency (%)
False 86295
74.5%
True 29492
 
25.5%
2025-07-17T15:31:01.600371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

new_visits
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing35392
Missing (%)30.6%
Memory size6.8 MiB
1.0
80395 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters241185
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 80395
69.4%
(Missing) 35392
30.6%

Length

2025-07-17T15:31:01.759043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T15:31:01.930823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 80395
100.0%

Most occurring characters

ValueCountFrequency (%)
1 80395
33.3%
. 80395
33.3%
0 80395
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 80395
33.3%
. 80395
33.3%
0 80395
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 80395
33.3%
. 80395
33.3%
0 80395
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 80395
33.3%
. 80395
33.3%
0 80395
33.3%

Interactions

2025-07-17T15:30:28.518378image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:19.617065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:21.535748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:22.960168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:24.358863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:25.689895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:27.174341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:28.714433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:19.806675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:21.698563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:23.210409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:24.533598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:26.025227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:27.394518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:28.954871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:19.988175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:21.864804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:23.402688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:24.691347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:26.179605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:27.643755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:29.149219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:20.162935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:22.064288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:23.546660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:24.858359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:26.356687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:27.827181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:29.358845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:20.367753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:22.292656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:23.754566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:25.058280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:26.604646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:27.990014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:29.507660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:21.100709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:22.529418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:23.933522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:25.251779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:26.792102image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:28.184518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:29.740623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:21.316110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:22.755058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:24.146142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:25.489799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:26.983521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-17T15:30:28.359756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-07-17T15:31:02.122553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
browserdevice.isMobiledeviceTypegclIdPresentgeoClustergeoNetwork.continentgeoNetwork.networkDomaingeoNetwork.subContinentospageViewspurchaseValuesessionIdsessionNumbersessionStarttotalHitstrafficSource.adwordsClickInfo.adNetworkTypetrafficSource.adwordsClickInfo.pagetrafficSource.adwordsClickInfo.slottrafficSource.campaigntrafficSource.mediumuserChanneluserId
browser1.0000.4650.3540.1330.0030.1670.0000.1200.5080.0140.0040.0740.0290.0740.0240.4450.0700.3060.0500.1000.1480.006
device.isMobile0.4651.0000.9990.1490.0000.0810.0000.1430.9940.0620.0130.1500.0310.1500.0860.4960.0170.4970.1970.2510.3190.000
deviceType0.3540.9991.0000.1510.0000.0710.0000.1140.7150.0450.0070.1070.0210.1070.0610.4970.0160.3520.1440.1780.2260.000
gclIdPresent0.1330.1490.1511.0000.0020.1300.0000.1360.1580.0000.0000.1870.0060.1870.0051.0001.0001.0000.9340.8770.8680.008
geoCluster0.0030.0000.0000.0021.0000.0000.0020.0010.0000.0000.0070.0040.0000.0040.0020.0000.0000.0000.0000.0000.0000.003
geoNetwork.continent0.1670.0810.0710.1300.0001.0000.0001.0000.1750.0480.0020.0730.0120.0730.0670.2600.0000.1760.0640.1530.2090.005
geoNetwork.networkDomain0.0000.0000.0000.0000.0020.0001.0000.0000.0030.0060.0000.0080.0000.0080.0050.0000.0130.0000.0000.0000.0000.000
geoNetwork.subContinent0.1200.1430.1140.1360.0011.0000.0001.0000.1310.0390.0000.0860.0060.0860.0530.3870.0000.2620.0340.1770.2250.005
os0.5080.9940.7150.1580.0000.1750.0030.1311.0000.0310.0000.0620.0140.0620.0440.5360.0340.3790.0540.1430.1930.005
pageViews0.0140.0620.0450.0000.0000.0480.0060.0390.0311.0000.690-0.0350.281-0.0350.9950.1000.0000.0690.0000.0210.0400.003
purchaseValue0.0040.0130.0070.0000.0070.0020.0000.0000.0000.6901.000-0.0370.351-0.0370.6841.0001.0001.0000.0000.0370.020-0.002
sessionId0.0740.1500.1070.1870.0040.0730.0080.0860.062-0.035-0.0371.0000.0211.000-0.0320.7930.1290.5490.1190.1450.146-0.001
sessionNumber0.0290.0310.0210.0060.0000.0120.0000.0060.0140.2810.3510.0211.0000.0210.2810.0140.0000.0180.0000.0630.037-0.001
sessionStart0.0740.1500.1070.1870.0040.0730.0080.0860.062-0.035-0.0371.0000.0211.000-0.0320.7930.1290.5490.1190.1450.146-0.001
totalHits0.0240.0860.0610.0050.0020.0670.0050.0530.0440.9950.684-0.0320.281-0.0321.0000.1480.0000.1010.0000.0300.0570.003
trafficSource.adwordsClickInfo.adNetworkType0.4450.4960.4971.0000.0000.2600.0000.3870.5360.1001.0000.7930.0140.7930.1481.0000.1490.9680.9990.2710.9990.017
trafficSource.adwordsClickInfo.page0.0700.0170.0161.0000.0000.0000.0130.0000.0340.0001.0000.1290.0000.1290.0000.1491.0000.1000.2440.0340.1490.014
trafficSource.adwordsClickInfo.slot0.3060.4970.3521.0000.0000.1760.0000.2620.3790.0691.0000.5490.0180.5490.1010.9680.1001.0000.6960.2560.9680.034
trafficSource.campaign0.0500.1970.1440.9340.0000.0640.0000.0340.0540.0000.0000.1190.0000.1190.0000.9990.2440.6961.0000.5660.5810.005
trafficSource.medium0.1000.2510.1780.8770.0000.1530.0000.1770.1430.0210.0370.1450.0630.1450.0300.2710.0340.2560.5661.0000.8650.008
userChannel0.1480.3190.2260.8680.0000.2090.0000.2250.1930.0400.0200.1460.0370.1460.0570.9990.1490.9680.5810.8651.0000.007
userId0.0060.0000.0000.0080.0030.0050.0000.0050.0050.003-0.002-0.001-0.001-0.0010.0030.0170.0140.0340.0050.0080.0071.000

Missing values

2025-07-17T15:30:30.395855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-17T15:30:31.687179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-17T15:30:33.738402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

trafficSource.isTrueDirectpurchaseValuebrowserdevice.screenResolutiontrafficSource.adContenttrafficSource.keywordscreenSizegeoClustertrafficSource.adwordsClickInfo.slotdevice.mobileDeviceBrandingdevice.mobileInputSelectoruserIdtrafficSource.campaigndevice.mobileDeviceMarketingNamegeoNetwork.networkDomaingclIdPresentdevice.operatingSystemVersionsessionNumberdevice.flashVersiongeoNetwork.regiontrafficSourcetotals.visitsgeoNetwork.networkLocationsessionIdosgeoNetwork.subContinenttrafficSource.mediumtrafficSource.adwordsClickInfo.isVideoAdbrowserMajorlocationCountrydevice.browserSizetrafficSource.adwordsClickInfo.adNetworkTypesocialEngagementTypegeoNetwork.citytrafficSource.adwordsClickInfo.pagegeoNetwork.metropageViewslocationZonedevice.mobileDeviceModeltrafficSource.referralPathtotals.bouncesdatedevice.languagedeviceTypeuserChanneldevice.browserVersiontotalHitsdevice.screenColorssessionStartgeoNetwork.continentdevice.isMobilenew_visits
0NaN0.0Edgenot available in demo datasetNaNNaNmediumRegion_2NaNnot available in demo datasetnot available in demo dataset61421(not set)not available in demo datasetdomain10not available in demo dataset1not available in demo datasetWashingtonyoutube.com1not available in demo dataset1500100799WindowsNorthern AmericareferralNaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially EngagedRedmondNaNSeattle-Tacoma WA1.08not available in demo dataset/intl/hr/yt/about/1.02017-07-14not available in demo datasetdesktopSocialnot available in demo dataset1not available in demo dataset1500100799AmericasFalse1.0
1True0.0Chromenot available in demo datasetNaNNaNmediumRegion_3NaNnot available in demo datasetnot available in demo dataset72287(not set)not available in demo datasetdomain30not available in demo dataset1not available in demo datasetCalifornia(direct)1not available in demo dataset1495262065MacintoshNorthern America(none)NaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially EngagedMountain ViewNaNSan Francisco-Oakland-San Jose CA1.08not available in demo datasetNaN1.02017-05-19not available in demo datasetdesktopDirectnot available in demo dataset1not available in demo dataset1495262065AmericasFalse1.0
2True0.0Chromenot available in demo datasetNaN(not provided)mediumRegion_2NaNnot available in demo datasetnot available in demo dataset25180(not set)not available in demo datasetdomain10not available in demo dataset2not available in demo datasetLombardygoogle1not available in demo dataset1508510328WindowsSouthern EuropeorganicNaNnot available in demo datasetItalynot available in demo datasetNaNNot Socially EngagedMilanNaN(not set)6.08not available in demo datasetNaNNaN2017-10-20not available in demo datasetdesktopOrganic Searchnot available in demo dataset6not available in demo dataset1508510328EuropeFalseNaN
3NaN0.0Internet Explorernot available in demo datasetNaNNaNmediumRegion_4NaNnot available in demo datasetnot available in demo dataset41295(not set)not available in demo datasetdomain30not available in demo dataset1not available in demo datasetnot available in demo datasetyoutube.com1not available in demo dataset1483431838WindowsEastern AsiareferralNaNnot available in demo datasetJapannot available in demo datasetNaNNot Socially Engagednot available in demo datasetNaNnot available in demo dataset1.08not available in demo dataset/yt/about/ja/1.02017-01-03not available in demo datasetdesktopSocialnot available in demo dataset1not available in demo dataset1483431838AsiaFalse1.0
4True88950000.0Chromenot available in demo datasetNaNNaNmediumRegion_3NaNnot available in demo datasetnot available in demo dataset113697(not set)not available in demo datasetdomain10not available in demo dataset1not available in demo datasetnot available in demo dataset(direct)1not available in demo dataset1475804633WindowsNorthern America(none)NaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially Engagednot available in demo datasetNaNnot available in demo dataset54.08not available in demo datasetNaNNaN2016-10-06not available in demo datasetdesktopDirectnot available in demo dataset66not available in demo dataset1475804633AmericasFalse1.0
5True28000000.0Chromenot available in demo datasetNaNNaNmediumRegion_4NaNnot available in demo datasetnot available in demo dataset36442(not set)not available in demo datasetdomain10not available in demo dataset2not available in demo datasetnot available in demo dataset(direct)1not available in demo dataset1505506252MacintoshNorthern America(none)NaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially Engagednot available in demo datasetNaNnot available in demo dataset32.08not available in demo dataset/NaN2017-09-15not available in demo datasetdesktopReferralnot available in demo dataset48not available in demo dataset1505506252AmericasFalseNaN
6NaN80510000.0Chromenot available in demo datasetNaNNaNmediumRegion_2NaNnot available in demo datasetnot available in demo dataset62247(not set)not available in demo datasetdomain30not available in demo dataset1not available in demo datasetnot available in demo dataset(direct)1not available in demo dataset1472053897MacintoshNorthern America(none)NaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially Engagednot available in demo datasetNaNnot available in demo dataset15.08not available in demo dataset/NaN2016-08-24not available in demo datasetdesktopReferralnot available in demo dataset17not available in demo dataset1472053897AmericasFalse1.0
7NaN0.0Chromenot available in demo datasetNaN(not provided)mediumRegion_5NaNnot available in demo datasetnot available in demo dataset39682(not set)not available in demo datasetdomain10not available in demo dataset1not available in demo datasetFederal Territory of Kuala Lumpurgoogle1not available in demo dataset1490756441AndroidSoutheast AsiaorganicNaNnot available in demo datasetMalaysianot available in demo datasetNaNNot Socially EngagedKuala LumpurNaN(not set)1.08not available in demo datasetNaN1.02017-03-28not available in demo datasetmobileOrganic Searchnot available in demo dataset1not available in demo dataset1490756441AsiaTrue1.0
8True0.0Chromenot available in demo datasetNaN(not provided)mediumRegion_2NaNnot available in demo datasetnot available in demo dataset55604(not set)not available in demo datasetdomain30not available in demo dataset4not available in demo datasetTokyogoogle1not available in demo dataset1495702257MacintoshEastern AsiaorganicNaNnot available in demo datasetJapannot available in demo datasetNaNNot Socially EngagedMinatoNaN(not set)4.08not available in demo datasetNaNNaN2017-05-25not available in demo datasetdesktopOrganic Searchnot available in demo dataset4not available in demo dataset1495702257AsiaFalseNaN
9NaN0.0Safarinot available in demo datasetNaN(not provided)mediumRegion_1NaNnot available in demo datasetnot available in demo dataset55519(not set)not available in demo datasetdomain10not available in demo dataset1not available in demo dataset(not set)google1not available in demo dataset1500868868iOSEastern AsiaorganicNaNnot available in demo datasetHong Kongnot available in demo datasetNaNNot Socially EngagedHong KongNaN(not set)1.08not available in demo datasetNaN1.02017-07-23not available in demo datasetmobileOrganic Searchnot available in demo dataset1not available in demo dataset1500868868AsiaTrue1.0
trafficSource.isTrueDirectpurchaseValuebrowserdevice.screenResolutiontrafficSource.adContenttrafficSource.keywordscreenSizegeoClustertrafficSource.adwordsClickInfo.slotdevice.mobileDeviceBrandingdevice.mobileInputSelectoruserIdtrafficSource.campaigndevice.mobileDeviceMarketingNamegeoNetwork.networkDomaingclIdPresentdevice.operatingSystemVersionsessionNumberdevice.flashVersiongeoNetwork.regiontrafficSourcetotals.visitsgeoNetwork.networkLocationsessionIdosgeoNetwork.subContinenttrafficSource.mediumtrafficSource.adwordsClickInfo.isVideoAdbrowserMajorlocationCountrydevice.browserSizetrafficSource.adwordsClickInfo.adNetworkTypesocialEngagementTypegeoNetwork.citytrafficSource.adwordsClickInfo.pagegeoNetwork.metropageViewslocationZonedevice.mobileDeviceModeltrafficSource.referralPathtotals.bouncesdatedevice.languagedeviceTypeuserChanneldevice.browserVersiontotalHitsdevice.screenColorssessionStartgeoNetwork.continentdevice.isMobilenew_visits
116013NaN0.0Safarinot available in demo datasetNaN(not provided)mediumRegion_5NaNnot available in demo datasetnot available in demo dataset5959(not set)not available in demo datasetdomain10not available in demo dataset1not available in demo datasetnot available in demo datasetgoogle1not available in demo dataset1505786899MacintoshNorthern AmericaorganicNaNnot available in demo datasetCanadanot available in demo datasetNaNNot Socially Engagednot available in demo datasetNaNnot available in demo dataset6.08not available in demo datasetNaNNaN2017-09-18not available in demo datasetdesktopOrganic Searchnot available in demo dataset9not available in demo dataset1505786899AmericasFalse1.0
116014True0.0Chromenot available in demo datasetNaNNaNmediumRegion_1NaNnot available in demo datasetnot available in demo dataset112383(not set)not available in demo datasetdomain20not available in demo dataset2not available in demo datasetTaipei City(direct)1not available in demo dataset1510214455WindowsEastern Asia(none)NaNnot available in demo datasetTaiwannot available in demo datasetNaNNot Socially Engaged(not set)NaN(not set)9.08not available in demo datasetNaNNaN2017-11-09not available in demo datasetdesktopDirectnot available in demo dataset9not available in demo dataset1510214455AsiaFalseNaN
116015NaN0.0Chromenot available in demo datasetNaN(not provided)mediumRegion_2NaNnot available in demo datasetnot available in demo dataset78504(not set)not available in demo datasetdomain20not available in demo dataset1not available in demo datasetnot available in demo datasetgoogle1not available in demo dataset1510939926WindowsNorthern AmericaorganicNaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially Engagednot available in demo datasetNaNnot available in demo dataset3.08not available in demo datasetNaNNaN2017-11-17not available in demo datasetdesktopOrganic Searchnot available in demo dataset3not available in demo dataset1510939926AmericasFalse1.0
116016True0.0Chromenot available in demo datasetNaNNaNmediumRegion_1NaNnot available in demo datasetnot available in demo dataset71872(not set)not available in demo datasetdomain20not available in demo dataset2not available in demo datasetCalifornia(direct)1not available in demo dataset1470997085WindowsNorthern America(none)NaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially EngagedSan FranciscoNaNSan Francisco-Oakland-San Jose CA32.08not available in demo datasetNaNNaN2016-08-12not available in demo datasetdesktopDirectnot available in demo dataset40not available in demo dataset1470997085AmericasFalseNaN
116017NaN0.0Chromenot available in demo datasetNaNNaNmediumRegion_2NaNnot available in demo datasetnot available in demo dataset107114(not set)not available in demo datasetdomain20not available in demo dataset1not available in demo datasetNorth Hollandanalytics.google.com1not available in demo dataset1513612893WindowsWestern EuropereferralNaNnot available in demo datasetNetherlandsnot available in demo datasetNaNNot Socially EngagedAmsterdamNaN(not set)1.08not available in demo dataset/analytics/web/1.02017-12-18not available in demo datasetdesktopReferralnot available in demo dataset1not available in demo dataset1513612893EuropeFalse1.0
116018NaN35180000.0Chromenot available in demo datasetNaNNaNmediumRegion_3NaNnot available in demo datasetnot available in demo dataset109014(not set)not available in demo datasetdomain10not available in demo dataset1not available in demo datasetNew York(direct)1not available in demo dataset1500318402MacintoshNorthern America(none)NaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially EngagedNew YorkNaNNew York NY26.08not available in demo dataset/NaN2017-07-17not available in demo datasetdesktopReferralnot available in demo dataset28not available in demo dataset1500318402AmericasFalse1.0
116019True0.0Chromenot available in demo datasetNaNNaNmediumRegion_5NaNnot available in demo datasetnot available in demo dataset66111(not set)not available in demo datasetdomain20not available in demo dataset1not available in demo datasetCalifornia(direct)1not available in demo dataset1478624150MacintoshNorthern America(none)NaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially EngagedMountain ViewNaNSan Francisco-Oakland-San Jose CA1.08not available in demo datasetNaN1.02016-11-08not available in demo datasetdesktopDirectnot available in demo dataset1not available in demo dataset1478624150AmericasFalse1.0
116020True0.0Chromenot available in demo datasetNaNNaNmediumRegion_1NaNnot available in demo datasetnot available in demo dataset97614(not set)not available in demo datasetdomain20not available in demo dataset2not available in demo datasetDelhiseroundtable.com1not available in demo dataset1470384216WindowsSouthern AsiareferralNaNnot available in demo datasetIndianot available in demo datasetNaNNot Socially EngagedNew DelhiNaN(not set)1.08not available in demo dataset/google-analytics-launches-public-demo-account-22482.html1.02016-08-05not available in demo datasetdesktopReferralnot available in demo dataset1not available in demo dataset1470384216AsiaFalseNaN
116021NaN0.0Chromenot available in demo datasetNaN(not provided)mediumRegion_5NaNnot available in demo datasetnot available in demo dataset71050(not set)not available in demo datasetdomain10not available in demo dataset1not available in demo datasetTennesseegoogle1not available in demo dataset1506953297WindowsNorthern AmericaorganicNaNnot available in demo datasetUnited Statesnot available in demo datasetNaNNot Socially EngagedNashvilleNaNNashville TN2.08not available in demo datasetNaNNaN2017-10-02not available in demo datasetdesktopOrganic Searchnot available in demo dataset2not available in demo dataset1506953297AmericasFalse1.0
116022True81470000.0Chromenot available in demo datasetNaN(not provided)mediumRegion_5NaNnot available in demo datasetnot available in demo dataset39773(not set)not available in demo datasetdomain20not available in demo dataset4not available in demo datasetnot available in demo datasetgoogle1not available in demo dataset1501474532AndroidNorthern AmericaorganicNaNnot available in demo datasetCanadanot available in demo datasetNaNNot Socially Engagednot available in demo datasetNaNnot available in demo dataset73.08not available in demo datasetNaNNaN2017-07-30not available in demo datasetmobileOrganic Searchnot available in demo dataset102not available in demo dataset1501474532AmericasTrueNaN